Prediction of TBM penetration rate using intact and mass rock properties (case study: Zagros long tunnel, Iran)

Mohammadi, Seyed Davoud; Torabi-Kaveh, Mehdi; Bayati, Mohsen

doi:10.1007/s12517-014-1465-0

Cited by 28 publications

(9 citation statements)

References 29 publications

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“…Statistical methods, such as MRA, SPSS, together with ANNs, are conducted to predict TBM performance [58,59]. For the penetration rate of TBM, the prediction accuracy of SVM, LMRA, and ANN are compared [60,61]. In order to predict the penetration rate and advance rate of TBM, Armaghani et al utilized an ANN, PSO-ANN, and ICA-ANN to make the prediction and compared the prediction ability of these methods [30,39].…”

Section: Roadheader Performance and Tbm Performancementioning

confidence: 99%

“…Nevertheless, the abovementioned conclusions can provide a reference in the tunnel engineering field. [21] WNN>ANN [22] GP>SVM>ANN [78] SVMFF>ANNFF [79] MLP>MRA [76] PSO-ANN>ANN [93] ANFIS>CFT>ANN [5] FLM>BPNN>MRA [20] ICA-ANN>ANN>LMRA [94] TL>RNN>SVM [49] PSO-SVR>PSO-BPNN>PSO-ELM [53] Stability BPNN>LRM [56] MLP>RBFNN [58] TBM performance BPNN>NMRA [59] ANN>SPSS [31] ANN>MRA [60] ANN>LMRA [30] ICA-ANN>PSO-ANN>ANN [61] SVM>ANN [39] PSO-ANN>ICA-ANN>ANN [24] Geological conditions ANN>XGBoost, CatBoost, RF, DT, SVR, KNN, BLR [18] Overbreak ANN>NMRA>LMRA [67] ANFIS>FLM>ANN>SVM>NMRA>L MRA [38] GA-ANN>ANN [68] ABC-ANN>ANN [40] ABC-ANN>ANN [11] Tunnel convergence MLP>RBFNN>MRA [36] SVM>ANN [35] MLP>MARS [69] MLP>SPSS [70] Rockburst and flying rocks PSO-ANN>ICA-ANN>GA-ANN Note: The '>' means the performance of the left model outperforms the right one.…”

Section: A Characteristics Of Ann-based Modelsmentioning

confidence: 99%

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Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Wang

Wei

et al. 2020

IEEE Access

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Due to the lack of living space and the increase in population, there has been a construction boom in the underground space to improve the quality of human life. Tunnel engineering plays a vital role in the development of underground space. In addition to traditional methods, some intelligent methods such as artificial neural networks (ANNs) have been applied to various problems in the tunnel domain in recent years. This paper systematically reviews the application of ANNs from different aspects of tunnel engineering. It reveals that the backpropagation algorithm (BPA) and Levenberg-Marquardt algorithm (LMA) are the most widely used. Due to the limitations of some original models, some scholars use optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the original ANNs to obtain better prediction results. A comparison between the ANN-based methods and methods like statistical methods is conducted. Finally, the following conclusions can be drawn: (1) The recommended ratio of the training set and test set is 3:1; (2) The advantage of optimized ANNs is not apparent when the optimization algorithm varies. Additionally, the performance of ANNs is always better than that of statistical methods.

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Section: Roadheader Performance and Tbm Performancementioning

confidence: 99%

Section: A Characteristics Of Ann-based Modelsmentioning

confidence: 99%

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Wang

Wei

et al. 2020

IEEE Access

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“…Zagros long tunnel (lot 2) with a length of 26 km and about 6.73 m diameter is located in Kermanshah Province in the west of Iran where the tunnel has been designed to transfer water from the Sirwan River to the tropical plains. Geological formations along the tunnel route mainly composed of dark gray shale, shaly limestone, and limestone masses [24]. Arabian plate compressional tectonic forces have created several faults and thrust faults with NW-SE trend in the study area.…”

Section: Zagros Tunnelmentioning

confidence: 99%

“…Arabian plate compressional tectonic forces have created several faults and thrust faults with NW-SE trend in the study area. The tunnel strike is NESW, so that cuts vertically the trend of region structures [24]. The area is located in the Zagros Fold-Thrust Belt [25].…”

Section: Zagros Tunnelmentioning

confidence: 99%

Using Intact Rock Brittleness for Assessing TBM Penetration

Yağız¹,

Yazitova²

2019

World Congress on Civil, Structural, and Environmental Engineering

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The paper reviews the history of the punch penetration test (PPT) as a laboratory method that is conducted on intact sample to measure the rock brittleness (BI). Further, the interaction of rock brittleness and penetration rate of tunnel boring machine (TBM) has been examined and some relationship was developed between the BI and TBM penetration. For this aim, four tunnel projects having various rock types were studied and then four different dataset is examined and discussed herein. It is stated that the test via BI can be perfectly utilized for assessing the boreability of rocks. In conclusion, it is found that there is a relationship between the BI and TBMs penetration with correlation coefficient (r) ranging from 0.34-0.81 for hard rocks condition; however this correlation is quite low (0.34) for weathered and very weak sedimentary rocks categorized as extremely low brittle. The obtained results can be accepted for hard rocks but it should be used with care for weathered weak rocks that does not show brittleness behavior under the indentor.

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“…Hassanpour et al [18] calculated TBM performance based on experience of TBM tunneling. Mohammadi et al [19] suggested the TBM penetration rate using intact and mass rock properties. Paltrinieri et al [20] studied the analysis and estimation of gripper TBM performances.…”

Section: Introductionmentioning

confidence: 99%

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Afradi

Ebrahimabadi

2020

SN Appl. Sci.

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Prediction of TBM penetration rate using intact and mass rock properties (case study: Zagros long tunnel, Iran)

Cited by 28 publications

References 29 publications

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Using Intact Rock Brittleness for Assessing TBM Penetration

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

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